{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9705ffc4",
   "metadata": {},
   "source": [
    "Analyzing papers about \"embeddings\" from CS Arxiv over the past 5 years to see if their occurrence has increased using [ArXiv API Wraper](https://pypi.org/project/arxiv/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffdb427c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import pylab\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import tqdm\n",
    "\n",
    "from arxiv import SortCriterion , Search, SortOrder\n",
    "import typing\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e31b78d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Arxiv API Search\n",
    "embedding_search = Search(\n",
    "  query = \"embeddings\",\n",
    "  max_results = float('inf'), # limit of 300k results from the API total\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e6261f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Preparing results for Pandas import\n",
    "embedding_dict = defaultdict(list, title=[],date=[],category=[])\n",
    "\n",
    "results = embedding_search.results()\n",
    "\n",
    "for result in tqdm.tqdm(results):\n",
    "  embedding_dict['title'].append(result.title)\n",
    "  embedding_dict['date'].append(result.published)\n",
    "  embedding_dict['category'].append(result.primary_category)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29d00179",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get default dict into Pandas for graphing\n",
    "embedding_df = pd.DataFrame.from_dict(embedding_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "732b7ca4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "embedding_df.to_csv('embeddings.csv', index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "187a4ea2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "embedding_df = pd.read_csv(\"embeddings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8e5185c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "                                                   title  \\\n",
       "0      Frustratingly Easy Meta-Embedding -- Computing...   \n",
       "1      Learning Meta-Embeddings by Using Ensembles of...   \n",
       "2      Discrete Word Embedding for Logical Natural La...   \n",
       "3      Hash Embeddings for Efficient Word Representat...   \n",
       "4                         Gender Bias in Meta-Embeddings   \n",
       "...                                                  ...   \n",
       "16195  ArchNet: Data Hiding Model in Distributed Mach...   \n",
       "16196  On the Role of Visual Cues in Audiovisual Spee...   \n",
       "16197  Multilayer Structures of Graphene and Pt Nanop...   \n",
       "16198  Gravitational couplings in ${\\cal N}=2$ hetero...   \n",
       "16199  GLUECoS : An Evaluation Benchmark for Code-Swi...   \n",
       "\n",
       "                            date        category  \n",
       "0      2018-04-14 18:30:01+00:00           cs.CL  \n",
       "1      2015-08-18 09:29:22+00:00           cs.CL  \n",
       "2      2020-08-26 16:15:18+00:00           cs.CL  \n",
       "3      2017-09-12 16:13:10+00:00           cs.CL  \n",
       "4      2022-05-19 21:20:47+00:00           cs.CL  \n",
       "...                          ...             ...  \n",
       "16195  2020-04-23 04:59:05+00:00           cs.LG  \n",
       "16196  2020-04-25 01:00:03+00:00           cs.LG  \n",
       "16197  2020-04-25 10:35:45+00:00  physics.app-ph  \n",
       "16198  2020-04-25 13:06:51+00:00          hep-th  \n",
       "16199  2020-04-26 13:28:34+00:00           cs.CL  \n",
       "\n",
       "[16200 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8851ed79",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2020-04</td>\n",
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       "      <td>2020-04-23 04:59:05+00:00</td>\n",
       "      <td>cs.LG</td>\n",
       "      <td>2020-04</td>\n",
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       "      <td>On the Role of Visual Cues in Audiovisual Spee...</td>\n",
       "      <td>2020-04-25 01:00:03+00:00</td>\n",
       "      <td>cs.LG</td>\n",
       "      <td>2020-04</td>\n",
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       "      <td>cs.CL</td>\n",
       "      <td>2020-04</td>\n",
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      ],
      "text/plain": [
       "                                                   title  \\\n",
       "0      Frustratingly Easy Meta-Embedding -- Computing...   \n",
       "1      Learning Meta-Embeddings by Using Ensembles of...   \n",
       "2      Discrete Word Embedding for Logical Natural La...   \n",
       "3      Hash Embeddings for Efficient Word Representat...   \n",
       "4                         Gender Bias in Meta-Embeddings   \n",
       "...                                                  ...   \n",
       "16193  A novel embedded min-max approach for feature ...   \n",
       "16194  TAEN: Temporal Aware Embedding Network for Few...   \n",
       "16195  ArchNet: Data Hiding Model in Distributed Mach...   \n",
       "16196  On the Role of Visual Cues in Audiovisual Spee...   \n",
       "16199  GLUECoS : An Evaluation Benchmark for Code-Swi...   \n",
       "\n",
       "                            date category created_month  \n",
       "0      2018-04-14 18:30:01+00:00    cs.CL       2018-04  \n",
       "1      2015-08-18 09:29:22+00:00    cs.CL       2015-08  \n",
       "2      2020-08-26 16:15:18+00:00    cs.CL       2020-08  \n",
       "3      2017-09-12 16:13:10+00:00    cs.CL       2017-09  \n",
       "4      2022-05-19 21:20:47+00:00    cs.CL       2022-05  \n",
       "...                          ...      ...           ...  \n",
       "16193  2020-04-21 09:40:38+00:00    cs.LG       2020-04  \n",
       "16194  2020-04-21 16:32:10+00:00    cs.CV       2020-04  \n",
       "16195  2020-04-23 04:59:05+00:00    cs.LG       2020-04  \n",
       "16196  2020-04-25 01:00:03+00:00    cs.LG       2020-04  \n",
       "16199  2020-04-26 13:28:34+00:00    cs.CL       2020-04  \n",
       "\n",
       "[7499 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only CS papers (maybe filter further to IR?)\n",
    "embedding_df = embedding_df.loc[embedding_df[\"category\"].str.startswith('cs')]\n",
    "# Compress to months\n",
    "embedding_df['created_month'] = pd.to_datetime(embedding_df['date']).dt.strftime('%Y-%m')\n",
    "embedding_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf34c817",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "plt.style.use('bmh')\n",
    "\n",
    "histo = embedding_df.groupby('created_month').size().reset_index()\n",
    "histo_filtered = histo\n",
    "histo_filtered.columns = ['created_month','published_articles']\n",
    "\n",
    "# Average Smoothing\n",
    "window = 12\n",
    "average_y = []\n",
    "for ind in range(len(histo['published_articles']) - window + 1):\n",
    "    average_y.append(np.mean(histo['published_articles'][ind:ind+window]))\n",
    "\n",
    "for ind in range(window - 1):\n",
    "    average_y.insert(0, np.nan)\n",
    "\n",
    "histo_filtered['average_articles'] = average_y\n",
    "histo_filtered['created_month'] = pd.to_datetime(histo_filtered['created_month'])\n",
    "histo_smoothed = histo_filtered[['created_month','average_articles']]\n",
    "histo_smoothed.head(50)\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.set_facecolor('white')\n",
    "histo_smoothed.plot(x=\"created_month\",x_compat=True,ax=ax);\n",
    "\n",
    "\n",
    "plt.title(\"'Embeddings' CS Papers by Month on ArXiv\")\n",
    "plt.ylabel(\"Papers submitted by date\",labelpad=10);\n",
    "plt.xlabel(\"Year\",labelpad=10);\n",
    "\n",
    "ax.get_legend().remove()\n",
    "\n",
    "plt.show()"
   ]
  }
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